Cell-free DNA TAPS provides multimodal information for early cancer detection.
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ABSTRACT:
SUBMITTER: Siejka-Zielinska P
PROVIDER: S-EPMC8442905 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Multimodal, genome-wide characterization of epigenetic and genetic information in circulating cell-free DNA (cfDNA) could enable more sensitive early cancer detection. However, due to technological challenges associated with DNA methylation sequencing in low input cfDNA samples, most studies have been limited by DNA damage caused by bisulfite sequencing, or the qualitative nature of enrichment-based sequencing. Recently, we developed TET-assisted Pyridine Borane Sequencing (TAPS), which is a mild, bisulfite-free method for base-resolution direct DNA methylation sequencing. Here we optimized TAPS for cfDNA (cfTAPS) to provide high-quality and high-depth whole-genome cell-free methylomes. We applied cfTAPS to 85 cfDNA samples from patients with hepatocellular carcinoma (HCC) or pancreatic ductal adenocarcinoma (PDAC) and non-cancer controls. From just 10 ng cfDNA (1-3 mL of plasma), we generated the most comprehensive cfDNA methylome to date. We demonstrated that cfTAPS provides multimodal information about cfDNA characteristics, including DNA methylation, tissue of origin, and DNA fragmentation. Integrated analysis of these epigenetic and genetic features enables accurate identification of early HCC and PDAC.
| EGAS00001004962 | EGA
Project description:Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.
| S-EPMC10567114 | biostudies-literature
Project description:Multimodal epigenetic characterization of cell-free DNA (cfDNA) could improve the performance of blood-based early cancer detection. However, integrative profiling of cfDNA methylome and fragmentome has been technologically challenging. Here, we adapt an enzyme-mediated methylation sequencing method for comprehensive analysis of genome-wide cfDNA methylation, fragmentation, and copy number alteration (CNA) characteristics for enhanced cancer detection. We apply this method to plasma samples of 497 healthy controls and 780 patients of seven cancer types and develop an ensemble classifier by incorporating methylation, fragmentation, and CNA features. In the test cohort, our approach achieves an area under the curve value of 0.966 for overall cancer detection. Detection sensitivity for early-stage patients achieves 73% at 99% specificity. Finally, we demonstrate the feasibility to accurately localize the origin of cancer signals with combined methylation and fragmentation profiling of tissue-specific accessible chromatin regions. Overall, this proof-of-concept study provides a technical platform to utilize multimodal cfDNA features for improved cancer detection.
| S-EPMC10533817 | biostudies-literature
Project description:BackgroundThe quantitative relationship between HER2 copy number and prognosis in HER2 positive adjuvant setting remain controversial, and few studies have focused on adjuvant setting to illustrate the potential clinical relevance of HER2 in cfDNA. Our study aim to develop a novel method in HER2 quantification and explore the relationship between HER2 copy number in primary tumors or cfDNA and prognosis in HER2 positive early breast cancer.MethodsTwo hundred and two patients with early breast cancer were prospectively included in a study where primary tumors, matching non-cancer breast tissue, corresponding plasma, and the plasma from 20 healthy volunteers were collected. Cox proportional hazard analysis was employed to determine the prognostic value of HER2 gene copy number in tissue and cfDNA. Tissue based nomograms and time-dependent decision curve analysis were used to evaluate the practicality of HER2 copy number stratification.ResultsHER2 amplification by CNVplex demonstrated a robust concordance with FISH (concordance 89.2%). A three-tiered system of tissue and a two-tiered system of cfDNA classification were shown to be independent prognostic factors. A tissue copy number-based nomogram was fitted and further evaluation revealed a good performance in discrimination (c statistic 0.801) and calibration.ConclusionsWe first report CNVplex as a viable alternative for HER2 detection. Quantitative evaluation of HER2 presents tremendous potential for use in risk stratification. We also uncover the potential for using HER2 copy number in cfDNA as a biomarker for prognosis in a HER2 positive adjuvant setting.
| S-EPMC8850316 | biostudies-literature
Project description:People with Li-Fraumeni syndrome (LFS) harbor a germline pathogenic variant in the TP53 tumor suppressor gene, face a near 100% lifetime risk of cancer, and routinely undergo intensive surveillance protocols. Liquid biopsy has become an attractive tool for a range of clinical applications, including early cancer detection. Here, we provide a proof-of-principle for a multimodal liquid biopsy assay that integrates a targeted gene panel, shallow whole-genome, and cell-free methylated DNA immunoprecipitation sequencing for the early detection of cancer in a longitudinal cohort of 89 LFS patients. Multimodal analysis increased our detection rate in patients with an active cancer diagnosis over uni-modal analysis and was able to detect cancer-associated signal(s) in carriers prior to diagnosis with conventional screening (positive predictive value = 67.6%, negative predictive value = 96.5%). Although adoption of liquid biopsy into current surveillance will require further clinical validation, this study provides a framework for individuals with LFS.SignificanceBy utilizing an integrated cell-free DNA approach, liquid biopsy shows earlier detection of cancer in patients with LFS compared with current clinical surveillance methods such as imaging. Liquid biopsy provides improved accessibility and sensitivity, complementing current clinical surveillance methods to provide better care for these patients. See related commentary by Latham et al., p. 23. This article is featured in Selected Articles from This Issue, p. 5.
| S-EPMC10784744 | biostudies-literature
Project description:Breast-cancer metastasis suppressor 1 (BRMS1) gene encodes for a predominantly nuclear protein that differentially regulates the expression of multiple genes, leading to suppression of metastasis without blocking orthotropic tumour growth. The aim of the present study was to evaluate for the first time the prognostic significance of BRMS1 promoter methylation in cell-free DNA (cfDNA) circulating in plasma of non-small cell lung cancer (NSCLC) patients. Towards this goal, we examined the methylation status of BRMS1 promoter in NSCLC tissues, matched adjacent non-cancerous tissues and corresponding cfDNA as well as in an independent cohort of patients with advanced NSCLC and healthy individuals.Methylation of BRMS1 promoter was examined in 57 NSCLC tumours and adjacent non-cancerous tissues, in cfDNA isolated from 48 corresponding plasma samples, in cfDNA isolated from plasma of 74 patients with advanced NSCLC and 24 healthy individuals.The BRMS1 promoter was highly methylated both in operable NSCLC primary tissues (59.6%) and in corresponding cfDNA (47.9%) but not in cfDNA from healthy individuals (0%), while it was also highly methylated in cfDNA from advanced NSCLC patients (63.5%). In operable NSCLC, Kaplan-Meier estimates were significantly different in favour of patients with non-methylated BRMS1 promoter in cfDNA, concerning both disease-free interval (DFI) (P=0.048) and overall survival (OS) (P=0.007). In advanced NSCLC, OS was significantly different in favour of patients with non-methylated BRMS1 promoter in their cfDNA (P=0.003). Multivariate analysis confirmed that BRMS1 promoter methylation has a statistical significant influence both on operable NSCLC patients' DFI time and OS and on advanced NSCLC patients' PFS and OS.Methylation of BRMS1 promoter in cfDNA isolated from plasma of NSCLC patients provides important prognostic information and merits to be further evaluated as a circulating tumour biomarker.
| S-EPMC3992488 | biostudies-other
Project description:Pancreatic cancer is often detected late, when curative therapies are no longer possible. Here, we present non-invasive detection of pancreatic ductal adenocarcinoma (PDAC) by 5-hydroxymethylcytosine (5hmC) changes in circulating cell free DNA from a PDAC cohort (n = 64) in comparison with a non-cancer cohort (n = 243). Differential hydroxymethylation is found in thousands of genes, most significantly in genes related to pancreas development or function (GATA4, GATA6, PROX1, ONECUT1, MEIS2), and cancer pathogenesis (YAP1, TEAD1, PROX1, IGF1). cfDNA hydroxymethylome in PDAC cohort is differentially enriched for genes that are commonly de-regulated in PDAC tumors upon activation of KRAS and inactivation of TP53. Regularized regression models built using 5hmC densities in genes perform with AUC of 0.92 (discovery dataset, n = 79) and 0.92-0.94 (two independent test sets, n = 228). Furthermore, tissue-derived 5hmC features can be used to classify PDAC cfDNA (AUC = 0.88). These findings suggest that 5hmC changes enable classification of PDAC even during early stage disease.
| S-EPMC7572413 | biostudies-literature
Project description:Screening for early-stage disease is vital for reducing colorectal cancer (CRC)-related mortality. Methylation of circulating tumor DNA has been previously used for various types of cancer screening. A novel cell-free DNA (cfDNA) methylation-based model which can improve the early detection of CRC is warranted. For our study, we collected 313 tissue and 577 plasma samples from patients with CRC, advanced adenoma (AA), non-AA and healthy controls. After quality control, 187 tissue DNA samples (91 non-malignant tissue from CRC patients, 26 AA and 70 CRC) and 489 plasma cfDNA samples were selected for targeted DNA methylation sequencing. We further developed a cfDNA methylation model based on 11 methylation biomarkers for CRC detection in the training cohort (area under curve [AUC] = 0.90 (0.85-0.94]) and verified the model in the validation cohort (AUC = 0.92 [0.88-0.96]). The cfDNA methylation model robustly detected patients pre-diagnosed with early-stage CRC (AUC = 0.90 [0.86-0.95]) or AA (AUC = 0.85 [0.78-0.91]). Here we established and validated a non-invasive cfDNA methylation model based on 11 DNA methylation biomarkers for the detection of early-stage CRC and AA. The utilization of the model in clinical practice may contribute to the early diagnosis of CRC.
| S-EPMC8486566 | biostudies-literature
Project description:The detection of plasma cell-free tumor DNA (ctDNA) is prognostic in colorectal cancer (CRC) and has potential for early prediction of disease recurrence. In clinical routine, ctDNA-based diagnostics are limited by the low concentration of ctDNA and error rates of standard next-generation sequencing (NGS) approaches. We evaluated the potential to increase the stability and yield of plasma cell-free DNA (cfDNA) for routine diagnostic purposes using different blood collection tubes and various manual or automated cfDNA extraction protocols. Sensitivity for low-level ctDNA was measured in KRAS-mutant cfDNA using an error-reduced NGS procedure. To test the applicability of rapid evaluation of ctDNA persistence in clinical routine, we prospectively analyzed postoperative samples of 67 CRC (stage II) patients. ctDNA detection was linear between 0.0045 and 45%, with high sensitivity (94%) and specificity (100%) for mutations at 0.1% VAF. The stability and yield of cfDNA were superior when using Streck BCT tubes and a protocol by Zymo Research. Sensitivity for ctDNA increased 1.5-fold by the integration of variant reads from triplicate PCRs and with PCR template concentration. In clinical samples, ctDNA persistence was found in ∼9% of samples, drawn 2 weeks after surgery. Moreover, in a retrospective analysis of 14 CRC patients with relapse during adjuvant therapy, we successfully detected ctDNA (median 0.38% VAF; range 0.18-5.04% VAF) in 92.85% of patients significantly prior (median 112 days) to imaging-based surveillance. Using optimized pre-analytical conditions, the detection of postoperative ctDNA is feasible with excellent sensitivity and allows the prediction of CRC recurrence in routine oncology testing.
| S-EPMC8766716 | biostudies-literature
Project description:Detecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel noninvasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise predictions with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequence and methylation information in plasma cfDNA whole-genome bisulfite sequencing (WGBS) data. DISMIR introduces a new feature termed as 'switching region' to define cancer-specific differentially methylated regions, which can enrich the cancer-related signal at read-resolution. DISMIR applies a deep learning model to predict the source of every single read based on its DNA sequence and methylation state and then predicts the risk that the plasma donor is suffering from cancer. DISMIR exhibited high accuracy and robustness on hepatocellular carcinoma detection by plasma cfDNA WGBS data even at ultralow sequencing depths. Further analysis showed that DISMIR tends to be insensitive to alterations of single CpG sites' methylation states, which suggests DISMIR could resist to technical noise of WGBS. All these results showed DISMIR with the potential to be a precise and robust method for low-cost early cancer detection.
| S-EPMC8575022 | biostudies-literature